Accurate position of roads is useful in many commercial and government applications ranging from real time vehicle navigation systems to the definition of boundaries (congressional, municipal, voting districts).
The MAF, or Master Address File, is designed to be a complete and current list of all addresses and locations where people live or work, covering an estimated 115 million residences, as well as 60 million businesses and other structures in the United States. TIGER®, or Topologically Integrated Geographic Encoding and Referencing data is a digital database that identifies the type, location and name of streets, rivers, railroads and other geographic features, and geospatially defines their relationships to each other, to the MAF addresses, and to numerous other entities. The two databases are maintained by the U.S. Census Bureau's Geography Division.
The MAF/TIGER Accuracy Improvement Program (MTAIP) is a major improvement to the quality and accuracy of the Census Bureau's digital geographic data which will be used by U.S. census takers in 2010 and beyond. The program will enable census takers to more precisely conduct their research and tabulations, and will ultimately result in an advanced, easy to update digital database that accurately reflects all of the nation's geographic census data. The MTAIP has a requirement to collect road centerlines at sufficient horizontal accuracy to support a final deliverable product of 5 meters (CE95).
Roughly ⅓ of all counties in the contiguous United States will require centerline collection. With over 11.3M kilometers of roads, the MTAIP will be collecting about 3.7M kilometers of centerline information. Assuming an average collection speed of 15 mph, there will be over 155,000 hours of collection time. Currently it is estimated that for every hour of collection time, another hour of post processing will be spent refining the data to meet the 5 m requirement. The labor costs could be approximately $18M over the lifetime of the program. Any automation that can be applied to reduce the touched labor costs will have a dramatic impact on the overall cost of the program. There are several commercial and government programs that are gathering centerline road data, but not to the scale or accuracy required by the Census Bureau.
The most common approach is to outfit a van with a Global Positioning System (GPS) receiver (combined with Inertial Navigation System (INS) for dead reckoning), drive the roads, and ignore the differences between the van location and the centerline. This approach does not meet the 5 m requirement for roads with more than 2 lanes. A second approach is to drive the roads twice (once in each direction), average the 2 collections, and ignore errors introduced by lane changes. This approach is cost prohibitive for the number of roads MTAIP is collecting. A third approach is to record lane changes during the collection, and apply an average lane width offset to the van location as a post-processing step. This approach requires a high level of attention on the part of the operator to reduce human-induced error (2% error means 46,500 miles potentially outside of the 5 m specification).
An example of a mobile mapping and data collection system that can map rail, highway and the transportation infrastructure (e.g., roads, signs, and bridges) while traveling at normal traffic speed is the GPSVan™ developed by the Center for Mapping at the Ohio State University. A Mobile Mapping System (MMS) can be defined as a moving platform, upon which multiple sensor/measurement systems have been integrated, to provide three-dimensional, near-continuous positioning of both the platform and simultaneously collected geo-spatial data. The Center for Mapping developed this technology, realizing that Geographic Information Systems (GIS) require up-to-date and high-quality spatial data to enhance the decision making process in transportation and urban planning. The GPSVan™ positioning module integrates the Global Positioning System (GPS) in the differential mode, and an independent Dead-Reckoning System (DRS) that records the vehicle's position during temporary GPS data outages (satellite signal blockage by trees or other obstructions). The positioning of the vehicle is good to 10 cm when GPS data is available at three-second intervals. GPS data outages of 30 s, 60 s and 120 s cause the positioning degradation to the level of 0.2 m, 0.4 m and 1.0 m, respectively. Additional attributes, i.e., road signs, bridges, etc., can be recorded by a system operator, using a PC keyboard, or the touch screen of the system's portable computer.
An imaging module of the GPSVan™ includes a stereo camera system that records stereo images of the roadway as the van moves down the highway. The stereo system is supplemented by an analog camera system that runs in continuous video mode, and captures a photographic log of the survey. Each video frame is time-tagged with the GPS signal, and geodetic coordinates (i.e., latitude, longitude and ellipsoidal height) are assigned to every image. Digital stereo pairs are processed in a post-mission mode to determine geodetic coordinates of objects such as road edges and centerlines, curbs, street signs, mile markers, etc., with a relative accuracy of 5-10 centimeters within 10-40 meters from the vehicle. The analog imagery provides information for urban planners and tax assessors, as well as the real estate and transportation industry. Data collected by the GPSVan™ can be converted into a format compatible with a GIS, and used by the rail and transportation authorities to establish management priorities, and control safety features, such as speed limits and location of the warning signs.
Similar to the Ohio State GPSVan™, LambdaTech and Transmap use forward and/or side looking stereo cameras coupled with GPS navigational equipment to map road features. Another company using similar features is Visat. One survey article is “Land Based Mobile Mapping Systems” by Cameron Ellum and Nase El-Sheimy published in Photogrammetric Engineering & Remote Sensing for January 2002. Also, a Swedish system called the PhotoBus is documented by Gillieron et al. in the 3rd International Symposium on Mobile Mapping Technology. The Photobus system performs a survey of the painted road centerline using a GPS and a Charge-Coupled Device (CCD) camera mounted on a roof rack extending over the left side of the vehicle. The image footprint is about 2.8 meters long and 2 meters wide.